This disclosure relates generally to diagnostic imaging and, more particularly, to an apparatus and method of de-noising and restoring signals in computed tomography (CT) imaging system.
Typically, in computed tomography (CT) imaging systems, an x-ray source emits a fan or cone-shaped beam toward a subject or object, such as a patient or a piece of luggage. Hereinafter, the terms “subject” and “object” shall include anything capable of being imaged. The beam, after being attenuated by the subject, impinges upon an array of radiation detectors. The intensity of the attenuated beam radiation received at the detector array is typically dependent upon the attenuation of the x-ray beam by the subject. Each detector element of the detector array produces a separate electrical signal indicative of the attenuated beam received by each detector element. The electrical signals are transmitted to a data processing system for analysis which ultimately produces an image.
Generally, the x-ray source and the detector array are rotated about the gantry within an imaging plane and around the subject. X-ray sources typically include x-ray tubes, which emit the x-ray beam at a focal point. X-ray detectors typically include a collimator for collimating x-ray beams received at the detector, a scintillator for converting x-rays to light energy adjacent the collimator, and photodiodes for receiving the light energy from the adjacent scintillator and producing electrical signals therefrom. Typically, each scintillator of a scintillator array converts x-rays to light energy. Each scintillator discharges light energy to a photodiode adjacent thereto. Each photodiode detects the light energy and generates a corresponding electrical signal. The outputs of the photodiodes are transmitted to the data processing system for image reconstruction. Imaging data may be obtained using x-rays that are generated at a single polychromatic energy. However, some systems may obtain multi-energy images that provide additional information for generating images.
During scanning to acquire projection data, it is generally desirable to reduce x-ray dose received by the subject, thus protocols have been developed that reduce x-ray tube power and patient exposure during image data acquisition. Also, gantry speeds in CT imaging generally continue to increase over time, in an effort to capture images in a shorter time period to reduce motion artifacts. Thus, as dose is reduced and as gantry speed increases, the general trend is to reconstruct images using lower amounts of photons passing through the image volume, resulting in a reduced signal-to-noise ratio (SNR). As such, the effect of statistical noise has thereby increased, resulting in an increased propensity for noise-induced artifacts. Thus, there is a need to account for statistical noise in CT scanners.
To account for noise, signal restoration has traditionally been performed using closed-form or iterative solutions that are essentially based on neighbor pixels. For instance, in a known closed-form solution, signal restoration is performed through a weighted average of its neighbor pixels, using linear or non-linear noise filtering or smoothing algorithms such as Gaussian smoothing, bi-lateral filtering, and the like. In a known iterative solution, noise is estimated using an iterative “cost-optimization” approach in which the noise is iteratively estimated based on the surrounding pixels.
A disadvantage of such methods, however, is that while noise is averaged out, the contrast among neighboring pixels is also averaged out. Thus, when these known methods are applied to signals having a low SNR, a blurred version of the original signal can result.
Therefore, it would be desirable to improve the estimate of statistical noise without blurring the original signal.